e-LEARNING AND SEMANTIC WEB
Alexandros Karakos
Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi, Greece
Keywords: e-Learning, Learning Object, Metadata, Web services, Semantic Web, Ontology.
Abstract: e-Learning is fast, relevant and just-in-time learning grown from the learning requirements of the new,
dynamically changing and distributed educational world. The term “Semantic Web” encompasses efforts to
build a new WWW architecture that supports content with formal semantics, which enables better
possibilities for searching and navigating through the cyberspace. As such, the Semantic Web represents a
promising technology for realizing e-Learning requirements. This paper presents an approach for
implementing the e-Learning scenario using Semantic Web technologies. It is primarily based on ontology-
based descriptions of content, context and structure of the learning materials and benefits the providing of
and accessing to the learning materials.
1 INTRODUCTION
There are two main types of players in the learning
economy: producers and consumers. Producers of
learning material use various design tools and other
software to produce different kinds of learning
material. Consumers use learning material created
by others (or themselves) to develop new content
packages. Producers make their learning material
available by placing them in different kinds of
repositories accessible from the Internet. Typically,
consumers are expected to search these repositories
using metadata (way of searching for learning
material, we define it better in the next sections).
There may be copyright and payment issues
associated with the reuse of learning materials.
Current development efforts with learning
materials are mostly concerned about metadata and
content packaging aspects (way of package the
learning content). There has not been any significant
work done so far in automating the discovery and
packaging of learning objects based on variables
such as learning objectives and learning outcomes.
There has also not been a significant amount of work
done in personalizing e-learning based on learning
materials developed and stored at arbitrary locations
on the Internet. This is largely because learning
materials are a relatively new phenomenon.
Automating these processes is also a knowledge-
intensive activity likely to require the application of
artificial intelligence techniques such as knowledge
representation and reasoning.
2 BASIC INFORMATION
ON e-LEARNING
A lot of attention has been devoted to educational
systems and electronic learning (“e-learning”) in
recent years, due to the fact that content and tool
support can now be offered at a widely affordable
level, both with respect to technical prerequisites
and pricing. Developed by researchers as well as
practitioners, many e-learning systems use the
Internet as an infrastructure to distribute content
more efficiently even in remote places, to present it,
and to ease administrative tasks.
But before continue analysing the distribution of
this e-learning content, it is essential to focus on the
content itself and how it is organised.
2.1 The Digital Resources
Digital Assets are the simplest form of Digital
Resources and they serve as the starting point for an
e-learning lifecycle. These assets can be of many
different types (e.g. graphics, images of simple text
documents) and can exist in several different
formats.
The steps involved in the transformation of
Digital Assets into a Learning Object are:
• Digital Asset A Digital Asset is defined as any
piece of content that is created using technology
(
Angad, 2005).
419
Karakos A. (2009).
e-LEARNING AND SEMANTIC WEB.
In Proceedings of the First International Conference on Computer Supported Education, pages 419-422
DOI: 10.5220/0001972404190422
Copyright
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• Compound Digital Assets Compound Digital
Assets can be best described as digital assets with
contextualised information.
• Learning Object There are many different
definitions for a Learning Object and there is no
general agreement to what constitutes a Learning
Object
. We have defined a Learning Object as the
aggregation of a Digital Asset, Compound Digital
Asset and Metadata with a particular learning
purpose. This definition incorporates a number of
definitions by other authors (
Hawryszkiewycz, 2002),
(
Dalziel, 2002), (Wiley, 2000) and (South, 2000).
• Complex Learning Object Complex Learning
Objects are packages consisting of structured
assemblies of zero or more Digital Assets, zero or
more Compound Digital Assets and one or more
Learning Objects (Dublin, 2003).
• Metadata in reality is data describing data and
it can be used to describe any digital resource.
2.2 e-Learning
A general agreement seems to exist regarding roles
played by people in a learning environment as well
as regarding the core functionality of modern e-
learning platforms (
Husemann, 2002). The main
players in these systems are the learners and the
authors; others include trainers and administrators.
Content consumed by learners and created by
authors is commonly handled, stored, and exchanged
in units named as learning objects (LOs) as we said.
The LOs can be accessed dynamically, e.g. over the
Web (
Vossen, 2002).
2.3 Metadata & e-Learning
Compared to traditional learning in which the
instructor plays the intermediate role between the
learner and the learning material, the learning
scenario in e-Learning is completely different:
instructors no longer control the delivery of material
and learners have a possibility to combine learning
material in courses on their own. So the content of
learning material must stand on its own. However,
regardless of the time or expense put into creating
advanced training material the content is useless
unless it can be searched and indexed easily. This is
especially true as the volume and types of learning
content increase, meta-data on the objects become a
critical factor. Indeed, meta-data are needed for an
appropriate description of learning objects so that
plug-and-play configuration of classes and courses is
possible. As we saw, several standardization efforts
have been launched, in order to reuse content from
one system to another.
3 WEB SERVICES
In essence, Web services are independent software
components that use the Internet as a communication
and composition infrastructure. They abstract from
the view of specific computers and provide a
service-oriented view by using standardized stack of
protocols. Web services can be combined to build
new ones with a more comprehensive functionality.
Even in terms of interoperation of business-to-
consumer (B2C) systems, Web services are currently
obtaining a growing importance.
In figure 1, the typical steps of an invocation of a
Web service are shown. In a first step, suppose that a
client needs to find a Web service which provides a
specific functionality. This is done by contracting a
UDDI registry (step 1), which returns the name of a
server (service provider) where an appropriate Web
service is hosted (step 2). Since the client still does
not know how to invoke the desired service, a
WSDL description is requested which contains the
name and the parameters of the operation(s) of the
service) step 3 and 4). The client is now able to
invoke the service using SOAP protocol, which
essentially puts the data in the envelope and sends it
over the Web by using HTTP. The service provider
receives the request and executes the desired
operation(s) on behalf of that client. The results are
finally sent back to the client by using SOAP over
HTTP again (step 6).
Figure 1: Invocation steps of web service.
4 SEMANTIC WEB SERVICES
4.1 Semantic Web Architecture
The term “Semantic Web” encompasses efforts to
build a new WWW architecture that supports
content with formal semantics. That means content
suitable for automated systems to consume, as
opposed to content intended for human
consumption. This will enable automated agents to
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reason about Web content, and produce an
intelligent response to unforeseen situations. Layers
of the Semantic Web «Expressing meaning» are the
main task of the Semantic Web. In order to achieve
several layers are needed (Berners-Lee, 2000). They
are presented in the figure 2, among which the
following layers are the basic ones:
• the XML layer, which represents data;
• the RDF layer, which represents the meaning of
data;
• the Ontology layer, which represents the formal
common agreement about meaning of data;
• the Logic layer, which enables intelligent
reasoning with meaningful data.
Figure 2: Layers of the semantic web architecture.
4.2 Ontology-based Metadata
The role of ontology is to formally describe shared
meaning of used vocabulary (set of symbols). In
fact, the ontology constrains the set of possible
mapping between symbols and their meanings. But
the shared understanding problem in e-Learning
occurs on several orthogonal levels, which describe
several aspects of document usage, as described in
figure 3.
Figure 3: Aspects of document usage.
From the student point of view the most
important things for searching learning materials
are: what the learning material is about (content) and
in which form this topic is presented (context).
However, while learning material does not appear in
isolation, another dimension (structure) is needed to
encompass a set of learning materials in a learning
course.
5 LEARNING SCENARIO
Based on the discussion in the previous section, this
section presents overall architecture of our ontology
based e-learning scenario. The architecture of the
system is represented in figure 4. The knowledge
warehouse acts as a metadata repository and the onto
broker system (
Decker, 1999) is a principal
differencing mechanism.
The first phase is the production of learning
materials that may be used or reused in the
construction of training courses. In order to provide
learning material, which could be suitable for
metadata-searching, each learning material has to be
described or "enriched" with the following metadata
information:
• what is the learning material about (content
annotation),
• which context has the learning material (context
annotation) and
• how is it connected to other learning materials
(structure annotation).
This "enriching" consists of explicitly adding to
each learning material a set of metadata information
referring to course ontology. Providing information
is for now constrained on manually entering
metadata information (facts) through automatically
generated templates, based on the definition of
concepts in the course ontology.
Figure 4: Architecture of an e-Learning portal.
6 CONCLUSIONS
“Making content machine-understandable” is a
popular paraphrase of the fundamental prerequisite
for the Semantic Web. In spite of its potential
philosophical ramifications this phrase must be
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421

taken very pragmatically: content (of whatever type
of media) is 'machine-understandable' if it is bound
(attached, pointing, etc.) to some formal description
of itself.
This vision requires development of new
technologies for web-friendly data description. The
Resource Description Framework (RDF) metadata
standard is a core technology used along with other
web technologies like XML. Ontologies are
(meta)data schemas, providing a controlled
vocabulary of concepts, each with an explicitly
defined and machine processable semantics. By
defining shared and common domain theories,
ontologies help both people and machines to
communicate concisely, supporting the exchange of
semantics and not only syntax.
In the same time, promising areas for applying
the Semantic Web are unlimited. In fact, each area,
in which a lot of information should be provided and
accessed in a distributed manner, searches for some
semantic-based solution.
In this paper we presented an e-learning scenario
that exploits ontologies in three ways:
• for describing the semantics (content) of the
learning materials. This is the domain dependent
ontology,
• for defining learning context of the learning
material and
• for structuring learning materials in the learning
courses.
This three-dimensional space enables easier and
more comfortable search and navigation through
learning material.
The purpose was to clarify possibilities of using
ontologies as a semantic backbone for e-learning.
Primarily, the objectives are to facilitate the
contribution of and efficient access to information.
But, in a broader or in Semantic Web's view, an
ontology-based learning process could be a relevant
(problem-dependent), a personalised (user-
customised) and an active (context-sensitive)
process. These are prerequisites for efficient learning
in the dynamically changed business. This new view
enables us to go a step further and consider or
interpret the learning process as a process of
managing knowledge in the right place, at the right
time, in the right manner in order to satisfy business
objectives - knowledge management. It means the
merging of e-learning and knowledge management
using the Semantic Web should be the promising
integration.
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